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   "source": [
    "### Project"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project you are given a file that contains some parking ticket violations for NYC.\n",
    "\n",
    "(It's just a tiny extract!)\n",
    "\n",
    "If you're wondering where I get these data sets, Kaggle is an **excellent** source of data sets in a whole variety of topics: \n",
    "https://www.kaggle.com/\n",
    "\n",
    "You have to sign up, but it's free.\n",
    "\n",
    "If you want the full data set, it's available here: https://www.kaggle.com/new-york-city/nyc-parking-tickets/version/2#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this sample data set, the file is named: \n",
    "```\n",
    "nyc_parking_tickets_extract.csv\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your goals are as follows:"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "##### Goal 1\n",
    "Create a lazy iterator that will return a named tuple of the data in each row. The data types should be appropriate - i.e. if the column is a date, you should be storing dates in the named tuple, if the field is an integer, then it should be stored as an integer, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Goal 2\n",
    "\n",
    "Calculate the number of violations by car make."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note:\n",
    "Try to use lazy evaluation as much as possible - it may not always be possible though! That's OK, as long as it's kept to a minimum."
   ]
  },
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